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What Do Big Data Tell Us about Why People Take Gig Economy Jobs?

Author

Listed:
  • Dmitri K. Koustas

Abstract

The gig economy is widely regarded to be a source of secondary or temporary income, but little is known about economic activity outside of the gig economy. Using data from a large, online personal finance application, I document the evolution of non-gig income and household balance sheets surrounding the participation decision for gig economy jobs. This simple analysis reveals striking pretrends in income and assets. In addition to providing insight into the reasons why households enter the gig economy, these findings have potentially important implications for the external validity of previous studies focusing on gig economy activity only.

Suggested Citation

  • Dmitri K. Koustas, 2019. "What Do Big Data Tell Us about Why People Take Gig Economy Jobs?," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 367-371, May.
    • Handle: RePEc:aea:apandp:v:109:y:2019:p:367-71
    Note: DOI: 10.1257/pandp.20191041
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    Citations

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    Cited by:

    1. Sung‐Hee Jeon & Huju Liu & Yuri Ostrovsky, 2021. "Measuring the gig economy in Canada using administrative data," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 54(4), pages 1638-1666, November.
    2. Jonathan Cribb & Xiaowei Xu, 2020. "Going solo: how starting solo self-employment affects incomes and well-being," IFS Working Papers W20/23, Institute for Fiscal Studies.
    3. Oliver Alexander & Jeff Borland & Andrew Charlton & Amit Singh, 2021. "Uber down under: The labour market for drivers in Australia," Melbourne Institute Working Paper Series wp2021n18, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    4. Adermon, Adrian & Hensvik, Lena, 2022. "Gig-jobs: Stepping stones or dead ends?," Labour Economics, Elsevier, vol. 76(C).
    5. Alexandre Mas & Amanda Pallais, 2020. "Alternative Work Arrangements," Annual Review of Economics, Annual Reviews, vol. 12(1), pages 631-658, August.
    6. Jeff Borland & Michael Coelli, 2023. "The Australian labour market and IT-enabled technological change," Melbourne Institute Working Paper Series wp2023n01, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    7. Maciej Berk{e}sewicz & Dagmara Nikulin & Marcin Szymkowiak & Kamil Wilak, 2021. "The gig economy in Poland: evidence based on mobile big data," Papers 2106.12827, arXiv.org.
    8. KURODA Sachiko & ONISHI Koichiro, 2023. "Exploring the Gig Economy in Japan: A bank data-driven analysis of food delivery gig workers," Discussion papers 23025, Research Institute of Economy, Trade and Industry (RIETI).
    9. Stefania Cosci & Valentina Meliciani & Marco Pini, 2021. "Historical roots of innovative entrepreneurial culture: the impact of firms using motive power in 1927 on Italian provincial start-up rate," CERBE Working Papers wpC38, CERBE Center for Relationship Banking and Economics.
    10. Oliver Alexander & Jeff Borland & Andrew Charlton & Amit Singh, 2022. "The Labour Market for Uber Drivers in Australia," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 55(2), pages 177-194, June.
    11. Kazakova, E. & Sandomirskaia, M. & Suvorov, A. & Khazhgerieva, A. & Shavshin, R., 2023. "Platforms, online labor markets, and crowdsourcing. Part 2. Crowdsourcing," Journal of the New Economic Association, New Economic Association, vol. 61(4), pages 128-144.
    12. Joshua D. Gottlieb & Avi Zenilman, 2020. "When Workers Travel: Nursing Supply During COVID-19 Surges," NBER Working Papers 28240, National Bureau of Economic Research, Inc.
    13. Dmitri Koustas, 2020. "Insights from New Tax-Based Measures of Gig Work in the United States," CESifo Forum, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 21(03), pages 5-9, September.
    14. Maria Cesira Urzi Brancati & Annarosa Pesole & Enrique Férnandéz-Macías, 2020. "New evidence on platform workers in Europe: Results from the second COLLEEM survey," JRC Research Reports JRC118570, Joint Research Centre.

    More about this item

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand

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